Using accumulating big datasets in public databases, we have developed a computational model for the prediction of drugs exerting sex-specific effects. Dissecting the differences between the sexes in health and disease is crucial for the improvement of the efficiency, efficacy, accuracy and precision of medical care. However, this has not received much attention in both medical care and clinical research. Thousands of drugs have been approved [
14], but systematic evaluation for sex-specific effects has been lacking. Computational approaches and frameworks for these purposes are still not readily available. The model presented here addresses this major gap. The accuracy of this model was confirmed by animal experimentation with two positive predictions for sex-specific effects, i.e. acebutolol and tacrine, and one negative prediction for sex-specific effects, i.e. metformin. Importantly, the clinical relevance of our model was demonstrated in studies with male and female patients receiving metoprolol, which showed that a significantly lower DBP was achieved in female than male patients.
In fact, in our attempt to validate the model in humans, we tried to employ publicly available clinical data to support the predictions. Sutandar et al. [
16] reported the acebutolol plus hydrochlorothiazide effects for the treatment of essential hypertension. Since detailed data were provided in the study for 11 male and 11 female patients with acebutolol treatment, we performed a meta-analysis for any differences between men and women, but we did not find significant effects in SBP (Additional file
5: Figure S3a), DBP (Additional file
5: Figure S3b) or HR (Additional file
5: Figure S3c). Analysis of absolute heart rate values revealed significant effects of acebutolol in both sexes. However, this study did not exclude the interference of combination with other drugs, such as diuretics, and the sample size was rather to small significant sex differences. To this extent, in multicenter clinical trials with acebutolol alone for the treatment of essential hypertension, SBP and DBP were lowered effectively at rest but not exercise [
17‐
20]. Another multicenter clinical trial reported significant effects of acebutolol and confirming its anti-hypertensive actions [
21]. However, none of these studies compared the responses to acebutolol between men and women. For our clinical validation, we chose metoprolol, due to its similar structure to acebutolol and that its use has been limited in China because of its side-effects. In first cohort, obese female patients got the more lowering blood pressure effect than lean male patients (overweight and obesity is a risk factor for cardiovascular diseases and diabetes), the great differences supported the sex-response of metoprolol. The second cohort’s study also showed the same sex-response of metoprolol in age, gender, BMI and biochemical parameters matched patients. Our data show for the first time that the response to this drug differs significantly between men and women, which is also a strong clinical confirmation for our model.
Our study was limited by scarce sex-specific genomic and transcriptomic data, especially, data on sex-specific responses to first-line drugs. This lack of data limited our prediction model in assessing sex-specific responses to further drugs. Another limitation is that gene expression profile based approaches do not address direct protein targets of the interested drugs. The expression profile-derived “gene signature” represent one type of “molecular phenotype” but not the drug targets. Although this limitation existed, this class of approaches has been successfully applied in a number of studies in recent years. In addition, genetic or genomic variations in drug target DNA regions could lead to changing in drug effects. Therefore, integrating GWAS data and eQTL data into this study could be useful to improve the prediction accuracy.